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21 lines
1.2 KiB
Text
21 lines
1.2 KiB
Text
Yet another general purpose Naive Bayesian classifier.
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(under heavy development)
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Naive Bayes Classifier is probably the most widely used text classifier,
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it's a supervised learning algorithm. It can be used to classify blog posts
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or news articles into different categories like sports, entertainment and
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so forth.
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Naive Bayes is a simple technique for constructing classifiers: models that
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assign class labels to problem instances, represented as vectors of feature
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values, where the class labels are drawn from some finite set. It is not a
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single algorithm for training such classifiers, but a family of algorithms
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based on a common principle: all naive Bayes classifiers assume that the value
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of a particular feature is independent of the value of any other feature,
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given the class variable. For example, a fruit may be considered to be an apple
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if it is red, round, and about 10 cm in diameter. A naive Bayes classifier
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considers each of these features to contribute independently to the probability
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that this fruit is an apple, regardless of any possible correlations between
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the color, roundness, and diameter features.
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WWW: https://pypi.python.org/pypi/naiveBayesClassifier
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